FlexPepDock lessons from CAPRI peptide-protein rounds and suggested new criteria for assessment of model quality and utility

Use of phosphotyrosine-containing peptides to target SH2 domains: Antagonist peptides of the Crk/CrkL-p130Cas axis

Protein-protein interactions between SH2 domains and segments of proteins that include a post-translationally phosphorylated tyrosine residue (pY) underpin numerous signal transduction cascades that allow cells to respond to their environment. Dysregulation of the writing, erasing, and reading of these posttranslational modifications is a hallmark of human disease, notably cancer. Elucidating the precise role of the SH2 domain-containing adaptor proteins Crk and CrkL in tumor cell migration and invasion is challenging because there are no specific and potent antagonists available. Crk and CrkL SH2s interact with a region of the docking protein p130Cas containing 15 potential pY-containing tetrapeptide motifs. This chapter summarizes recent efforts toward peptide antagonists for this Crk/CrkL-p130Cas interaction. We describe our protocol for recombinant expression and purification of Crk and CrkL SH2s for functional assays and our procedure to determine the consensus binding motif from the p130Cas sequence. To develop a more potent antagonist, we employ methods often associated with structure-based drug design. Computational docking using Rosetta FlexPepDock, which accounts for peptides having a greater number of conformational degrees of freedom than small organic molecules that typically constitute libraries, provides quantitative docking metrics to prioritize candidate peptides for experimental testing. A battery of biophysical assays, including fluorescence polarization, differential scanning fluorimetry and saturation transfer difference nuclear magnetic resonance spectroscopy, were employed to assess the candidates. In parallel, GST pulldown competition assays characterized protein-protein binding in vitro. Taken together, our methodology yields peptide antagonists of the Crk/CrkL-p130Cas axis that will be used to validate targets, assess druggability, foster in vitro assay development, and potentially serve as lead compounds for therapeutic intervention.

From In Silico to a Cellular Model: Molecular Docking Approach to Evaluate Antioxidant Bioactive Peptides

The increasing need to counteract the redox imbalance in chronic diseases leads to focusing research on compounds with antioxidant activity. Among natural molecules with health-promoting effects on many body functions, bioactive peptides are gaining interest. They are protein fragments of 2–20 amino acids that can be released by various mechanisms, such as gastrointestinal digestion, food processing and microbial fermentation. Recent studies report the effects of bioactive peptides in the cellular environment, and there is evidence that these compounds can exert their action by modulating specific pathways. This review focuses on the newest approaches to the structure–function correlation of the antioxidant bioactive peptides, considering their molecular mechanism, by evaluating the activation of specific signaling pathways that are linked to antioxidant systems. The correlation between the results of in silico molecular docking analysis and the effects in a cellular model was highlighted. This knowledge is fundamental in order to propose the use of bioactive peptides as ingredients in functional foods or nutraceuticals.

Opportunistic Challenges of Computer-aided Drug Discovery of Lipopeptides: New Insights for Large Molecule Therapeutics

Computer-aided drug designing is a promising approach to defeating the dry pipeline of drug discovery. It aims at reduced experimental efforts with cost-effectiveness. Naturally occurring large molecules with molecular weight higher than 500 Dalton such as cationic peptides, cyclic peptides, glycopeptides and lipopeptides are a few examples of large molecules which have successful applications as the broad spectrum antibacterial, anticancer, antiviral, antifungal and antithrombotic drugs. Utilization of microbial metabolites as potential drug candidates incur cost effectiveness through large scale production of such molecules rather than a synthetic approach. Computational studies on such compounds generate tremendous possibilities to develop novel leads with challenges to handle these complex molecules with available computational tools. The opportunities begin with the desired structural modifications in the parent drug molecule. Virtual modifications followed by molecular interaction studies at the target site through molecular modeling simulations and identification of structure-activity relationship models to develop more prominent and potential drug molecules. Lead optimization studies to develop novel compounds with increased specificity and reduced off targeting is a big challenge computationally for large molecules. Prediction of optimized pharmacokinetic properties facilitates development of a compound with lower toxicity as compared to the natural compounds. Generating the library of compounds and studies for target specificity and ADMET (Absorption, Distribution, Metabolism, Excretion and Toxicity) for large molecules are laborious and incur huge cost and chemical wastage through in-vitro methods. Hence, computational methods need to be explored to develop novel compounds from natural large molecules with higher specificity. This review article is focusing on possible challenges and opportunities in the pathway of computer-aided drug discovery of large molecule therapeutics.

Insights into protein-DNA interactions from hydrogen bond energy-based comparative protein-ligand analyses

Hydrogen bonds play important roles in protein folding and protein-ligand interactions, particularly in specific protein-DNA recognition. However, the distributions of hydrogen bonds, especially hydrogen bond energy (HBE) in different types of protein-ligand complexes, is unknown. Here we performed a comparative analysis of hydrogen bonds among three non-redundant datasets of protein-protein, protein-peptide, and protein-DNA complexes. Besides comparing the number of hydrogen bonds in terms of types and locations, we investigated the distributions of HBE. Our results indicate that while there is no significant difference of hydrogen bonds within protein chains among the three types of complexes, interfacial hydrogen bonds are significantly more prevalent in protein-DNA complexes. More importantly, the interfacial hydrogen bonds in protein-DNA complexes displayed a unique energy distribution of strong and weak hydrogen bonds whereas majority of the interfacial hydrogen bonds in protein-protein and protein-peptide complexes are of predominantly high strength with low energy. Moreover, there is a significant difference in the energy distributions of minor groove hydrogen bonds between protein-DNA complexes with different binding specificity. Highly specific protein-DNA complexes contain more strong hydrogen bonds in the minor groove than multi-specific complexes, suggesting important role of minor groove in specific protein-DNA recognition. These results can help better understand protein-DNA interactions and have important implications in improving quality assessments of protein-DNA complex models.

Harnessing protein folding neural networks for peptide-protein docking

Highly accurate protein structure predictions by deep neural networks such as AlphaFold2 and RoseTTAFold have tremendous impact on structural biology and beyond. Here, we show that, although these deep learning approaches have originally been developed for the in silico folding of protein monomers, AlphaFold2 also enables quick and accurate modeling of peptide-protein interactions. Our simple implementation of AlphaFold2 generates peptide-protein complex models without requiring multiple sequence alignment information for the peptide partner, and can handle binding-induced conformational changes of the receptor. We explore what AlphaFold2 has memorized and learned, and describe specific examples that highlight differences compared to state-of-the-art peptide docking protocol PIPER-FlexPepDock. These results show that AlphaFold2 holds great promise for providing structural insight into a wide range of peptide-protein complexes, serving as a starting point for the detailed characterization and manipulation of these interactions.

Predicting Protein-Peptide Complex Structures by Accounting for Peptide Flexibility and the Physicochemical Environment

Predicting protein-peptide complex structures is crucial to the understanding of a vast variety of peptide-mediated cellular processes and to peptide-based drug development. Peptide flexibility and binding mode ranking are the two major challenges for protein-peptide complex structure prediction. Peptides are highly flexible molecules, and therefore, brute-force modeling of peptide conformations of interest in protein-peptide docking is beyond current computing power. Inspired by the fact that the protein-peptide binding process is like protein folding, we developed a novel strategy, named MDockPeP2, which tries to address these challenges using physicochemical information embedded in abundant monomeric proteins with an exhaustive search strategy, in combination with an integrated global search and a local flexible minimization method. Only the peptide sequence and the protein crystal structure are required. The method was systemically assessed using a newly constructed structural database of 89 nonredundant protein-peptide complexes with the peptide sequence length ranging from 5 to 29 in which about half of the peptides are longer than 15 residues. MDockPeP2 yielded a total success rate of 58.4% (70.8, 79.8%) for the bound docking (i.e., with the bound receptor and fully flexible peptides) and 19.0% (44.8, 70.7%) for the challenging unbound docking when top 10 (100, 1000) models were considered for each prediction. MDockPeP2 achieved significantly higher success rates on two other datasets, peptiDB and LEADS-PEP, which contain only short- and medium-size peptides (≤ 15 residues). For peptiDB, our method obtained a success rate of 62.0% for the bound docking and 35.9% for the unbound docking when the top 10 models were considered. For LEADS-PEP, MDockPeP2 achieved a success rate of 69.8% when the top 10 models were considered. The program is available at https://zougrouptoolkit.missouri.edu/mdockpep2/download.html.

In silico Approaches for the Design and Optimization of Interfering Peptides Against Protein-Protein Interactions

Large contact surfaces of protein-protein interactions (PPIs) remain to be an ongoing issue in the discovery and design of small molecule modulators. Peptides are intrinsically capable of exploring larger surfaces, stable, and bioavailable, and therefore bear a high therapeutic value in the treatment of various diseases, including cancer, infectious diseases, and neurodegenerative diseases. Given these promising properties, a long way has been covered in the field of targeting PPIs via peptide design strategies. In silico tools have recently become an inevitable approach for the design and optimization of these interfering peptides. Various algorithms have been developed to scrutinize the PPI interfaces. Moreover, different databases and software tools have been created to predict the peptide structures and their interactions with target protein complexes. High-throughput screening of large peptide libraries against PPIs; "hotspot" identification; structure-based and off-structure approaches of peptide design; 3D peptide modeling; peptide optimization strategies like cyclization; and peptide binding energy evaluation are among the capabilities of in silico tools. In the present study, the most recent advances in the field of in silico approaches for the design of interfering peptides against PPIs will be reviewed. The future perspective of the field and its advantages and limitations will also be pinpointed.

PyRosetta Jupyter Notebooks Teach Biomolecular Structure Prediction and Design

Biomolecular structure drives function, and computational capabilities have progressed such that the prediction and computational design of biomolecular structures is increasingly feasible. Because computational biophysics attracts students from many different backgrounds and with different levels of resources, teaching the subject can be challenging. One strategy to teach diverse learners is with interactive multimedia material that promotes self-paced, active learning. We have created a hands-on education strategy with a set of sixteen modules that teach topics in biomolecular structure and design, from fundamentals of conformational sampling and energy evaluation to applications like protein docking, antibody design, and RNA structure prediction. Our modules are based on PyRosetta, a Python library that encapsulates all computational modules and methods in the Rosetta software package. The workshop-style modules are implemented as Jupyter Notebooks that can be executed in the Google Colaboratory, allowing learners access with just a web browser. The digital format of Jupyter Notebooks allows us to embed images, molecular visualization movies, and interactive coding exercises. This multimodal approach may better reach students from different disciplines and experience levels as well as attract more researchers from smaller labs and cognate backgrounds to leverage PyRosetta in their science and engineering research. All materials are freely available at https://github.com/RosettaCommons/PyRosetta.notebooks.

Modeling beta-sheet peptide-protein interactions: Rosetta FlexPepDock in CAPRI rounds 38-45

Peptide-protein docking is challenging due to the considerable conformational freedom of the peptide. CAPRI rounds 38-45 included two peptide-protein interactions, both characterized by a peptide forming an additional beta strand of a beta sheet in the receptor. Using the Rosetta FlexPepDock peptide docking protocol we generated top-performing, high-accuracy models for targets 134 and 135, involving an interaction between a peptide derived from L-MAG with DLC8. In addition, we were able to generate the only medium-accuracy models for a particularly challenging target, T121. In contrast to the classical peptide-mediated interaction, in which receptor side chains contact both peptide backbone and side chains, beta-sheet complementation involves a major contribution to binding by hydrogen bonds between main chain atoms. To establish how binding affinity and specificity are established in this special class of peptide-protein interactions, we extracted PeptiDBeta, a benchmark of solved structures of different protein domains that are bound by peptides via beta-sheet complementation, and tested our protocol for global peptide-docking PIPER-FlexPepDock on this dataset. We find that the beta-strand part of the peptide is sufficient to generate approximate and even high resolution models of many interactions, but inclusion of adjacent motif residues often provides additional information necessary to achieve high resolution model quality.

Flexible docking of peptides to proteins using CABS-dock

Molecular docking of peptides to proteins can be a useful tool in the exploration of the possible peptide binding sites and poses. CABS-dock is a method for protein-peptide docking that features significant conformational flexibility of both the peptide and the protein molecules during the peptide search for a binding site. The CABS-dock has been made available as a web server and a standalone package. The web server is an easy to use tool with a simple web interface. The standalone package is a command-line program dedicated to professional users. It offers a number of advanced features, analysis tools and support for large-sized systems. In this article, we outline the current status of the CABS-dock method, its recent developments, applications, and challenges ahead.

Protocols for All-Atom Reconstruction and High-Resolution Refinement of Protein-Peptide Complex Structures

Structural characterizations of protein-peptide complexes may require further improvements. These may include reconstruction of missing atoms and/or structure optimization leading to higher accuracy models. In this work, we describe a workflow that generates accurate structural models of peptide-protein complexes starting from protein-peptide models in C-alpha representation generated using CABS-dock molecular docking. First, protein-peptide models are reconstructed from their C-alpha traces to all-atom representation using MODELLER. Next, they are refined using Rosetta FlexPepDock. The described workflow allows for reliable all-atom reconstruction of CABS-dock models and their further improvement to high-resolution models.

Protein-peptide docking using CABS-dock and contact information

CABS-dock is a computational method for protein-peptide molecular docking that does not require predefinition of the binding site. The peptide is treated as fully flexible, while the protein backbone undergoes small fluctuations and, optionally, large-scale rearrangements. Here, we present a specific CABS-dock protocol that enhances the docking procedure using fragmentary information about protein-peptide contacts. The contact information is used to narrow down the search for the binding peptide pose to the proximity of the binding site. We used information on a single-chosen and randomly chosen native protein-peptide contact to validate the protocol on the peptiDB benchmark. The contact information significantly improved CABS-dock performance. The protocol has been made available as a new feature of the CABS-dock web server (at http://biocomp.chem.uw.edu.pl/CABSdock/).

SHORT ABSTRACT

CABS-dock is a tool for flexible docking of peptides to proteins. In this article, we present a protocol for CABS-dock docking driven by information about protein-peptide contact(s). Using information on individual protein-peptide contacts allows to improve the accuracy of CABS-dock docking.

Protein-peptide docking: opportunities and challenges

Peptides have recently attracted much attention as promising drug candidates. Rational design of peptide-derived therapeutics usually requires structural characterization of the underlying protein-peptide interaction. Given that experimental characterization can be difficult, reliable computational tools are needed. In recent years, a variety of approaches have been developed for 'protein-peptide docking', that is, predicting the structure of the protein-peptide complex, starting from the protein structure and the peptide sequence, including variable degrees of information about the peptide binding site and/or conformation. In this review, we provide an overview of protein-peptide docking methods and outline their capabilities, limitations, and applications in structure-based drug design. Key challenges are also briefly discussed, such as modeling of large-scale conformational changes upon binding, scoring of predicted models, and optimal inclusion of varied types of experimental data and theoretical predictions into an integrative modeling process.

re-TAMD: exploring interactions between H3 peptide and YEATS domain using enhanced sampling

BACKGROUND

Analysis of preferred binding regions of a ligand on a protein is important for detecting cryptic binding pockets and improving the ligand selectivity.

RESULT

The enhanced sampling approach TAMD has been adapted to allow a ligand to unbind from its native binding site and explore the protein surface. This so-called re-TAMD procedure was then used to explore the interaction between the N terminal peptide of histone H3 and the YEATS domain. Depending on the length of the peptide, several regions of the protein surface were explored. The peptide conformations sampled during the re-TAMD correspond to peptide free diffusion around the protein surface.

CONCLUSIONS

The re-TAMD approach permitted to get information on the relative influence of different regions of the N terminal peptide of H3 on the interaction between H3 and YEATS.

High-resolution global peptide-protein docking using fragments-based PIPER-FlexPepDock

Peptide-protein interactions contribute a significant fraction of the protein-protein interactome. Accurate modeling of these interactions is challenging due to the vast conformational space associated with interactions of highly flexible peptides with large receptor surfaces. To address this challenge we developed a fragment based high-resolution peptide-protein docking protocol. By streamlining the Rosetta fragment picker for accurate peptide fragment ensemble generation, the PIPER docking algorithm for exhaustive fragment-receptor rigid-body docking and Rosetta FlexPepDock for flexible full-atom refinement of PIPER docked models, we successfully addressed the challenge of accurate and efficient global peptide-protein docking at high-resolution with remarkable accuracy, as validated on a small but representative set of peptide-protein complex structures well resolved by X-ray crystallography. Our approach opens up the way to high-resolution modeling of many more peptide-protein interactions and to the detailed study of peptide-protein association in general. PIPER-FlexPepDock is freely available to the academic community as a server at http://piperfpd.furmanlab.cs.huji.ac.il.

Peptide-protein interactions are crucial components of various important biological processes in living cells. High-resolution structural information of such interactions provides insight about the underlying biophysical principles governing the interactions, and a starting point for their targeted manipulations. Accurate docking algorithms can help fill the gap between the vast number of these interactions and the small number of experimentally solved structures. However, the accuracies of the existing protocols have been limited, in particular for ab initio docking when no information about the peptide beyond its sequence is available. Here we introduce PIPER-FlexPepDock, a fragment-based global docking protocol for high-resolution modeling of peptide-protein interactions. Integration of accurate and efficient representation of the peptide using fragment ensembles, their fast and exhaustive rigid-body docking, and their subsequent accurate flexible refinement, enables peptide-protein docking of remarkable accuracy. The validation on a representative benchmark set of crystallographically solved high-resolution peptide-protein complexes demonstrates significantly improved performance over all existing docking protocols. This opens up the way to the modeling of many more peptide-protein interactions, and to a more detailed study of peptide-protein association in general.

Web-accessible molecular modeling with Rosetta: The Rosetta Online Server that Includes Everyone (ROSIE)

The Rosetta molecular modeling software package provides a large number of experimentally validated tools for modeling and designing proteins, nucleic acids, and other biopolymers, with new protocols being added continually. While freely available to academic users, external usage is limited by the need for expertise in the Unix command line environment. To make Rosetta protocols available to a wider audience, we previously created a web server called Rosetta Online Server that Includes Everyone (ROSIE), which provides a common environment for hosting web-accessible Rosetta protocols. Here we describe a simplification of the ROSIE protocol specification format, one that permits easier implementation of Rosetta protocols. Whereas the previous format required creating multiple separate files in different locations, the new format allows specification of the protocol in a single file. This new, simplified protocol specification has more than doubled the number of Rosetta protocols available under ROSIE. These new applications include pK_a determination, lipid accessibility calculation, ribonucleic acid redesign, protein-protein docking, protein-small molecule docking, symmetric docking, antibody docking, cyclic toxin docking, critical binding peptide determination, and mapping small molecule binding sites. ROSIE is freely available to academic users at http://rosie.rosettacommons.org.